Depth Sensing Array Calibration Using Color Image Data
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Solution Overview
Problem
Depth mapping systems face challenges in accurately identifying no-depth regions and recalibrating sensing elements due to thermal, mechanical, and optical changes, leading to inefficient resource allocation and potential misidentification of recalibration needs.
Innovation Solution
The system incorporates a radiation source and an array of sensing elements, with processing and control circuitry that uses ancillary image data from a color image sensor to identify no-depth regions and recalibrate the sensing elements by selecting new areas for signal processing, employing deep learning networks for probability computation and super-pixel grouping to enhance signal-to-background ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system continuously monitors and recalibrates sensing elements to maintain accuracy, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The system performs preliminary identification of no-depth regions using color image data before attempting recalibration. By pre-classifying regions as no-depth (using color information to identify sky, water, or other depth-less areas) or requiring recalibration, the system avoids unnecessary recalibration operations and reduces overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces color image data as an intermediary to assist in identifying no-depth regions. This intermediary data source enables the system to differentiate between regions that genuinely require recalibration and those that are simply no-depth regions, thereby reducing unnecessary recalibration operations and system complexity.
2Reliability
If the system processes all sensing element signals to ensure complete coverage, then measurement completeness is improved, but processing time and energy consumption increase
Solution Approach 1:
The system extracts and processes only the signals from sensing elements corresponding to regions identified as no-depth or requiring recalibration. By taking out and processing only the necessary subset of signals rather than all signals, the system maintains measurement completeness for relevant regions while significantly reducing processing time and energy consumption.
Solution Approach 2:
The patent applies local quality by treating different regions of the sensing array differently based on their characteristics. Regions identified as no-depth or requiring recalibration receive specialized processing, while other regions are handled with standard processing, optimizing the balance between completeness and processing efficiency.
3Measurement precision
If the system uses multiple sensing elements to improve signal accuracy, then measurement precision is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system segments the sensing array into distinct regions based on color image data analysis. By dividing the array into no-depth regions and regions requiring recalibration, the system simplifies the detection and measurement process while maintaining precision through targeted processing of relevant segments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively identifies no-depth regions and optimizes recalibration, reducing resource wastage and improving depth mapping accuracy by utilizing ancillary image data and deep learning techniques to differentiate between recalibration needs and no-depth areas.
Implementation Method 1
A commonly-used technique to determine the distance to each point on the target scene involves transmitting one or more pulsed optical beams towards the target scene, followed by the measurement of the round-trip time, i.e. time-of-flight (ToF), taken by the optical beams as they travel from the source to the target scene and back to a detector array adjacent to the source.
Implementation Method 2
analyzing the reflected optical signal
Implementation Method 3
Objective optics are configured to form a first image of the target scene on the array of sensing elements
Implementation Method 4
Some ToF systems use single-photon avalanche diodes (SPADs), also known as Geiger-mode avalanche photodiodes (GAPDs), in measuring photon arrival time
Data Source
AI summary
Imaging apparatus (22) includes a radiation source (40), which emits pulsed beams (42) of optical radiation toward a target scene (24). An array (52) of sensing elements (78) output signals indicative of respective times of incidence of photons in a first image of the target scene that is formed on the array of sensing elements. An image sensor (64) captures a second image of the target scene in registration with the first image. Processing and control circuitry (56, 58) identifies, responsively to the signals, areas of the array on which the pulses of optical radiation reflected from corresponding regions of the target scene are incident, and processes the signals from the sensing elements in the identified areas in order measure depth coordinates of the corresponding regions of the target scene based on the times of incidence, while identifying, responsively to the second image, one or more of the regions of the target scene as no-depth regions.